The dynamics of role evolution in online learning communities

Abstract

Understanding the various roles and their corresponding evolving patterns is essential for evaluating the health of learning communities, such as in Asynchronous Online Discussion (AOD) forums. Existing research primarily focuses on identifying specific roles without considering the dynamics of role evolution, which are significant to ongoing community development. Guided by the Reader-to-Leader Framework (RtLF), this study employs multiple machine learning approaches to identify and predict the evolution of roles over time in a learning community. Our approach was applied to a K-12 Math online learning community, using data from 51,821 students to evaluate the community’s health over two years. First, a Gaussian Mixture Model was used to cluster students into five groups that represent their roles roughly in RtLF using the first nine months of learning activities. We then trained an XGBoost model to classify learner roles for subsequent periods based on meta-data. Third, we evaluate the health of the community by comparing the distribution of different roles over time and argue it is a healthy community. This study demonstrates how machine learning approaches can identify learner roles and support the health and development of learning communities.